You work as a machine learning specialist for the highway toll collection division of the regional state area. The toll collection division uses cameras to identify car license plates as the cars pass through the various toll gates on the state highways. You are on the team that is using SageMaker Image Classification machine learning to read and classify license plates by state and then identify the actual license plate number.
Very rarely, cars pass through the toll gates with plates from foreign countries, for example, Great Britain or Mexico. The outliers must not adversely affect your model’s predictions.
Which hyperparameter should you set, and to what value, to ensure these outliers do not adversely impact your model?

Answer options:

A.feature_dim set to 5
B.feature_dim set to 1
C.sample_size set to 10
D.sample_size set to 100
E.learning_rate set to 0.1
F.learning_rate set to 0.75

Answer: E
Option A is incorrect. The feature_dim hyperparameter is a setting on the K-Means and K-Nearest Neighbors algorithms, not the Image Classification algorithm.
Option B is incorrect. The feature_dim hyperparameter is a setting on the K-Means and K-Nearest Neighbors algorithms, not the Image Classification algorithm.
Option C is incorrect. The sample_size hyperparameter is a setting on the K-Nearest Neighbors algorithm, not the Image Classification algorithm.
Option D is incorrect. The sample_size hyperparameter is a setting on the K-Nearest Neighbors algorithm, not the Image Classification algorithm.
Option E is correct. The learning_rate hyperparameter governs how quickly the model adapts to new or changing data. Valid values range from 0.0 to 1.0. Setting this hyperparameter to a low value, such as 0.1, will make the model learn more slowly and be less sensitive to outliers. This is what you want. You want your model not to be adversely impacted by outlier data.
Option F is incorrect. The learning_rate hyperparameter governs how quickly the model adapts to new or changing data. Valid values range from 0.0 to 1.0. Setting this hyperparameter to a high value, such as 0.75, will make the model learn more quickly but be sensitive to outliers. This is not what you want. You want your model not to be adversely impacted by outlier data.
Reference:
Please see the Amazon SageMaker developer guide titled Image Classification Hyperparameters, and the Amazon SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms.